A Hybrid Anomaly Classification with Deep Learning (DL) and Binary Algorithms (BA) as Optimizer in the Intrusion Detection System (IDS)-Reference-Cited by-同舟云学术

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A Hybrid Anomaly Classification with Deep Learning (DL) and Binary Algorithms (BA) as Optimizer in the Intrusion Detection System (IDS)

Published:2020-02 Issue: Volume: Page:
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Container-title:2020 16th IEEE International Colloquium on Signal Processing & Its Applications (CSPA)
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Author:

Atefi Kayvan,Hashim Habibah,Khodadadi Touraj

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9052211/9068525/09068725.pdf?arnumber=9068725

Cited by 22 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

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2. Meta‐analysis and systematic review for anomaly network intrusion detection systems: Detection methods, dataset, validation methodology, and challenges;IET Networks;2024-06-18

3. A Systematic Literature Review on Swarm Intelligence Based Intrusion Detection System: Past, Present and Future;Archives of Computational Methods in Engineering;2024-03-01

4. Towards an Efficient DDoS Attack Detection in SDN: An Approach with CNN-GRU Fusion;2024 Fourth International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT);2024-01-11

5. Automated and Improved Detection of Cyber Attacks via an Industrial IDS Probe;IFIP Advances in Information and Communication Technology;2024

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